Applied Microeconomics
Applied Microeconomics
The Applied Microeconomics research group unites researchers working on a broad array of topics within such areas as labour economics, economics of education, health economics, family economics, urban economics, environmental economics, and the economics of science and innovation. The group operates in close collaboration with the CAGE Research Centre.
The group participates in the CAGE seminar on Applied Economics, which runs weekly on Tuesdays at 2:15pm. Students and faculty members of the group present their ongoing work in two brown bag seminars, held weekly on Tuesdays and Wednesdays at 1pm. Students, in collaboration with faculty members, also organise a bi-weekly reading group in applied econometrics on Thursdays at 1pm. The group organises numerous events throughout the year, including the Research Away Day and several thematic workshops.
Our activities
Work in Progress seminars
Tuesdays and Wednesdays 1-2pm
Students and faculty members of the group present their work in progress in two brown bag seminars. See below for a detailed scheduled of speakers.
Applied Econometrics reading group
Thursdays (bi-weekly) 1-2pm
Organised by students in collaboration with faculty members. See the Events calendar below for further details
People
Academics
Academics associated with the Applied Microeconomics Group are:
Research Students
Events
Econometrics & Statistics Seminar - Wen Zhou (NYU)
Title: Identification of Informative Core Structures in Weighted Directed Networks with Uncertainty Quantification
Abstract: In network analysis, noises and biases, which are often introduced by peripheral or non-essential components, can mask pivotal structures and hinder the efficacy of many network modeling and inference procedures. Recognizing this, identification of the core--periphery (CP) structure has emerged as a crucial data pre-processing step. While the identification of the CP structure has been instrumental in pinpointing core structures within networks, its application to directed weighted networks has been underexplored. Many existing efforts either fail to account for the directionality or lack the theoretical justification of the identification procedure. In this work, we seek answers to three pressing questions: (i) How to distinguish the informative and noninformative structures in weighted directed networks? (ii) What approach offers computational efficiency in discerning these components? (iii) Upon the detection of CP structure, can uncertainty be quantified to evaluate the detection? We adopt the signal-plus-noise model, categorizing different types of noninformative relational patterns, by which we define the sender and receiver peripheries. Furthermore, instead of confining the core component to a specific structure, we consider it complementary to either the sender or receiver peripheries. Based on our definitions on the sender and receiver peripheries, we propose spectral algorithms to identify the CP structure in directed weighted networks. Our algorithm stands out with statistical guarantees, ensuring the identification of sender and receiver peripheries with overwhelming probability. Additionally, we propose a hypothesis testing framework to infer CP structure upon detection. Our methods scale effectively for expansive directed networks. Implementing our methodology on faculty hiring network data revealed captivating insights into the informative structures and distinctions between informative and noninformative sender/receiver nodes across various academic disciplines.
This is a joint work with Wenqin Du, Tianxi Li, and Lihua Lei.